[skip ci] Update llms.md (#18464)

### Ticket
N/A

### Problem description
Order of sections was incorrect.

### What's changed
Section order corrected.

### Checklist
- [ ] [All post
commit](https://github.com/tenstorrent/tt-metal/actions/workflows/all-post-commit-workflows.yaml)
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- [ ] [Blackhole Post
commit](https://github.com/tenstorrent/tt-metal/actions/workflows/blackhole-post-commit.yaml)
CI passes (if applicable)
- [ ] [Model
regression](https://github.com/tenstorrent/tt-metal/actions/workflows/perf-models.yaml)
CI passes (if applicable)
- [ ] [Device performance
regression](https://github.com/tenstorrent/tt-metal/actions/workflows/perf-device-models.yaml)
CI passes (if applicable)
- [ ] **(For models and ops writers)** Full [new models
tests](https://github.com/tenstorrent/tt-metal/actions/workflows/full-new-models-suite.yaml)
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- [ ] New/Existing tests provide coverage for changes

tech_reports/LLMs/llms.md

@@ -209,7 +209,7 @@ ttnn_gamma_rm = ttnn.as_tensor(
 
 The distributed implementation is designed for cases where activations are **sharded along the embedding dimension** across multiple devices. It ensures the correct computation of mean and variance across shards by leveraging cross-device communication. Both interleaved and width-sharded inputs are supported.
 
-##### 2.3.2.2.1 Steps to Perform Distributed Normalization on TT-Devices
+##### 2.3.1.2.1 Steps to Perform Distributed Normalization on TT-Devices
 
 1. **Compute Local Statistics** - Each device computes the required statistics (e.g., \(E[x]\), \(E[x^2]\)) locally on its shard of the input tensor.
    - For **RMSNorm**, only \(E[x^2]\) is required.